title: How robust are probabilistic models of higher-level cognition?
abstract: An increasingly popular theory holds that the mind should be viewed as a near-optimal or rational engine of probabilistic inference, in domains as diverse as word learning, pragmatics, naive physics, and predictions of the future. We argue that this view, often identified with Bayesian models of inference, is markedly less promising than widely believed, and is undermined by post hoc practices that merit wholesale reevaluation. We also show that the common equation between probabilistic and rational or optimal is not justified.
authors.affiliation: New York University.
authors.forename: Gary F
authors.initials: GF
authors.lastname: Marcus
authors.forename: Ernest
authors.initials: E
authors.lastname: Davis
mesh_terms: D003071:Cognition / Q000502:physiology*; D056344:Executive Function / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D015233:Models, Statistical
keywords: Bayesian models; cognition(s); optimality